PDF ingestion via form: indexing in Qdrant, semantic…

The system automates PDF document ingestion from a web form, splits them into chunks, generates embeddings via local Ollama models, and stores them in the Qdrant vector database. On receiving a trigger from an MCP client, it performs semantic search and returns relevant content—functioning as a RAG backend for AI agents.

## Who it´s for
- AI agent developers using MCP to access documents
- Companies deploying internal RAG systems with local models
- MLOps engineers building semantic search over private PDFs

## What the automation does
- Receives PDF files via HTTP webhook from a web form
- Splits documents into chunks and generates embeddings using Ollama
- Stores vector representations in Qdrant for fast retrieval
- Performs semantic search on indexed documents upon MCP trigger
- Returns most relevant text snippets in response to queries
- Enables autonomous access to knowledge without human intervention

## What´s included
- Ready-to-use n8n workflow with LangChain and MCP support
- Logic for handling webhooks and form submissions
- Integrations with Ollama, Qdrant, and Web Form API
- Basic textual guide for setup and adaptation

## Requirements for setup
- Access to an n8n instance (self-hosted or cloud)
- Running Ollama server with a selected model (e.g., llama3)
- Installed and configured Qdrant vector database
- Web form that sends data via HTTP request
- Basic understanding of vector databases and APIs

## Benefits and outcomes
- Automatic indexing of technical docs and manuals
- Fast information access for AI agents via MCP
- Fully local data storage — no risk of private content leaks
- Reduced support load through autonomous search
- Scalable solution for enterprise knowledge bases
- Offline operation possible with local models

## Important: template only
Important: you are purchasing a ready-made automation workflow template only. Rollout into your infrastructure, connecting specific accounts and services, 1:1 setup help, custom adjustments for non-standard stacks and any consulting support are provided as a separate paid service at an individual rate. To discuss custom work or 1:1 help, contact via Telegram: @gleb923.